submission_id: mistralai-mixtral-8x7b-instruct-v0-1_v3
developer_uid: robert_irvine
status: deployed
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
reward_repo: rirv938/reward_gpt2_medium_preference_24m_e2
generation_params: {'temperature': 1.0, 'top_p': 1.0, 'top_k': 50, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['</s>', '<|user|>', '###', '\n'], 'max_input_tokens': 512, 'best_of': 4, 'max_output_tokens': 64}
formatter: {'memory_template': '<s>[INST] This is an entertaining conversation. You are {bot_name} who has the persona: {memory}.\nPlay the role of {bot_name}. Engage in a chat with {user_name} while staying in character. You should create a fun dialogue which entertains {user_name}. \n', 'prompt_template': '{prompt}\n', 'bot_template': '{bot_name}: {message}</s>', 'user_template': '[INST]{user_name}: {message}[/INST]', 'response_template': '{bot_name}:'}
reward_formatter: {'memory_template': 'Memory: {memory}\n', 'prompt_template': '{prompt}\n', 'bot_template': 'Bot: {message}\n', 'user_template': 'User: {message}\n', 'response_template': 'Bot:'}
timestamp: 2024-03-17T22:15:09+00:00
model_name: mistralai-mixtral-8x7b-instruct-v0-1_v3
model_eval_status: pending
safety_score: None
entertaining: None
stay_in_character: None
user_preference: None
double_thumbs_up: 5655
thumbs_up: 8293
thumbs_down: 3558
num_battles: 513225
num_wins: 272124
win_ratio: 0.530223586146427
celo_rating: 1174.22
Resubmit model
Running pipeline stage MKMLizer
Starting job with name mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer
Waiting for job on mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer to finish
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ _____ __ __ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ /___/ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ Version: 0.8.6 ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ The license key for the current software has been verified as ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ belonging to: ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ Chai Research Corp. ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ Expiration: 2024-04-15 23:59:59 ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ║ ║
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'list_files_info' (from 'huggingface_hub.hf_api') is deprecated and will be removed from version '0.23'. Use `list_repo_tree` and `get_paths_info` instead.
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: warnings.warn(warning_message, FutureWarning)
Retrying (%r) after connection broken by '%r': %s
Retrying (%r) after connection broken by '%r': %s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Downloaded to shared memory in 71.064s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: quantizing model to /dev/shm/model_cache
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Saving flywheel model at /dev/shm/model_cache
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: quantized model in 57.798s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Processed model mistralai/Mixtral-8x7B-Instruct-v0.1 in 134.496s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: creating bucket guanaco-mkml-models
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/special_tokens_map.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/tokenizer_config.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/tokenizer.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/tokenizer.model s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/tokenizer.model
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/config.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/flywheel_model.3.safetensors s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/flywheel_model.3.safetensors
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/flywheel_model.0.safetensors
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/flywheel_model.2.safetensors s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/flywheel_model.2.safetensors
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /dev/shm/model_cache/flywheel_model.1.safetensors s3://guanaco-mkml-models/mistralai-mixtral-8x7b-instruct-v0-1-v3/flywheel_model.1.safetensors
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: loading reward model from rirv938/reward_gpt2_medium_preference_24m_e2
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Please use `token` instead.
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: warnings.warn(
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:720: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: warnings.warn(
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:466: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: warnings.warn(
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: /opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: return self.fget.__get__(instance, owner)()
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Saving model to /tmp/reward_cache/reward.tensors
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Saving duration: 0.225s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Processed model rirv938/reward_gpt2_medium_preference_24m_e2 in 3.893s
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: creating bucket guanaco-reward-models
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: Bucket 's3://guanaco-reward-models/' created
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: uploading /tmp/reward_cache to s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/config.json s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/config.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/tokenizer_config.json s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/tokenizer_config.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/merges.txt s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/merges.txt
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/special_tokens_map.json s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/special_tokens_map.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/vocab.json s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/vocab.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/tokenizer.json s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/tokenizer.json
mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer: cp /tmp/reward_cache/reward.tensors s3://guanaco-reward-models/mistralai-mixtral-8x7b-instruct-v0-1-v3_reward/reward.tensors
Job mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer completed after 225.07s with status: succeeded
Stopping job with name mistralai-mixtral-8x7b-instruct-v0-1-v3-mkmlizer
Pipeline stage MKMLizer completed in 226.38s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.37s
Running pipeline stage ISVCDeployer
Creating inference service mistralai-mixtral-8x7b-instruct-v0-1-v3
Waiting for inference service mistralai-mixtral-8x7b-instruct-v0-1-v3 to be ready
Inference service mistralai-mixtral-8x7b-instruct-v0-1-v3 ready after 293.8180766105652s
Pipeline stage ISVCDeployer completed in 300.51s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.8489952087402344s
Received healthy response to inference request in 2.123744010925293s
Received healthy response to inference request in 2.140990734100342s
Received healthy response to inference request in 2.364474296569824s
Received healthy response to inference request in 1.7580890655517578s
5 requests
0 failed requests
5th percentile: 1.8312200546264648
10th percentile: 1.904351043701172
20th percentile: 2.050613021850586
30th percentile: 2.1271933555603026
40th percentile: 2.1340920448303224
50th percentile: 2.140990734100342
60th percentile: 2.2303841590881346
70th percentile: 2.319777584075928
80th percentile: 2.461378479003906
90th percentile: 2.6551868438720705
95th percentile: 2.7520910263061524
99th percentile: 2.829614372253418
mean time: 2.2472586631774902
Pipeline stage StressChecker completed in 14.00s
Running pipeline stage DaemonicModelEvalScorer
Pipeline stage DaemonicModelEvalScorer completed in 0.12s
Running M-Eval for topic stay_in_character
Running pipeline stage DaemonicSafetyScorer
M-Eval Dataset for topic stay_in_character is loaded
Pipeline stage DaemonicSafetyScorer completed in 0.27s
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%s, retrying in %s seconds...

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